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Only a few more months to Paris...

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Barry Wellman, FRSC               Director, NetLab Network
Founder, International Network for Social Network Analysis

Kyle Lowry is My Spirit Animal
Step by step, link by link, putting it together--Streisand/Sondheim
The earth to be spannd, connected by network--Walt Whitman
It's Always Something--Roseanne Roseannadanna
A day like all days, filled with those events that alter and illuminate our times--You Are There! 

NETWORKED: The New Social Operating System  Lee Rainie & Barry Wellman http://amzn.to/zXZg39
http://www.chass.utoronto.ca/~wellman            https://en.wikipedia.org/wiki/Barry_Wellman


-------- Forwarded Message --------
Subject: Latest Complexity Digest Posts
Date: Mon, 10 Feb 2020 12:03:05 +0000
From: Complexity Digest <[log in to unmask]>
Reply-To: [log in to unmask]
To: Barry <[log in to unmask]>


Learn about the latest and greatest related to complex systems research. More at https://unam.us4.list-manage.com/track/click?u=0eb0ac9b4e8565f2967a8304b&id=21d8d65dca&e=55e25a0e3e


Friendship paradox biases perceptions in directed networks

Nazanin Alipourfard, Buddhika Nettasinghe, Andrés Abeliuk, Vikram Krishnamurthy & Kristina Lerman
Nature Communications volume 11, Article number: 707 (2020)


Social networks shape perceptions by exposing people to the actions and opinions of their peers. However, the perceived popularity of a trait or an opinion may be very different from its actual popularity. We attribute this perception bias to friendship paradox and identify conditions under which it appears. We validate the findings empirically using Twitter data. Within posts made by users in our sample, we identify topics that appear more often within users’ social feeds than they do globally among all posts. We also present a polling algorithm that leverages the friendship paradox to obtain a statistically efficient estimate of a topic’s global prevalence from biased individual perceptions. We characterize the polling estimate and validate it through synthetic polling experiments on Twitter data. Our paper elucidates the non-intuitive ways in which the structure of directed networks can distort perceptions and presents approaches to mitigate this bias.

Source: www.nature.com (https://unam.us4.list-manage.com/track/click?u=0eb0ac9b4e8565f2967a8304b&id=b72c06d8f4&e=55e25a0e3e)



Multilayer modeling of adoption dynamics in energy demand management

Chaos 30, 013153 (2020); https://unam.us4.list-manage.com/track/click?u=0eb0ac9b4e8565f2967a8304b&id=5d526937c1&e=55e25a0e3e
Iacopo Iacopini, Benjamin Schäfer, Elsa Arcaute, Christian Beck, and Vito Latora


The electricity system is in the midst of large transformations, and new business models have emerged quickly to facilitate new modes of operation of the electricity supply. The so-called demand response seeks to coordinate demand from a large number of users through incentives, which are usually economic such as variable pricing tariffs. Here, we propose a simple mathematical framework to model consumer behaviors under demand response. Our model considers at the same time social influence and customer benefits to opt into and stay within new control schemes. In our model, information about the existence of a contract propagates through the links of a social network, while the geographic proximity of clusters of adopters influences the likelihood of participation by decreasing the likelihood of opting out. The results of our work can help to make informed decisions in energy demand management.

Source: aip.scitation.org (https://unam.us4.list-manage.com/track/click?u=0eb0ac9b4e8565f2967a8304b&id=d401178461&e=55e25a0e3e)



Enhanced Ability of Information Gathering May Intensify Disagreement Among Groups

Hiroki Sayama


Today's society faces widening disagreement and conflicts among constituents with incompatible views. Escalated views and opinions are seen not only in radical ideology or extremism but also in many other scenes of our everyday life. Here we show that widening disagreement among groups may be linked to the advancement of information communication technology, by analyzing a mathematical model of population dynamics in a continuous opinion space. We adopted the interaction kernel approach to model enhancement of people's information gathering ability and introduced a generalized non-local gradient as individuals' perception kernel. We found that the characteristic distance between population peaks becomes greater as the wider range of opinions becomes available to individuals or the greater attention is attracted to opinions distant from theirs. These findings may provide a possible explanation for why disagreement is growing in today's increasingly interconnected society, without attributing
its cause only to specific individuals or events.

Source: arxiv.org (https://unam.us4.list-manage.com/track/click?u=0eb0ac9b4e8565f2967a8304b&id=ae3c2c5b65&e=55e25a0e3e)



Antifragility Predicts the Robustness and Evolvability of Biological Networks through Multi-class Classification with a Convolutional Neural Network

Hyobin Kim, Stalin Muñoz, Pamela Osuna, Carlos Gershenson


Robustness and evolvability are essential properties to the evolution of biological networks. To determine if a biological network is robust and/or evolvable, the comparison of its functions before and after mutations is required. However, it has an increasing computational cost as network size grows. Here we aim to develop a predictor to estimate the robustness and evolvability of biological networks without an explicit comparison of functions. We measure antifragility in Boolean network models of biological systems and use this as the predictor. Antifragility is a property to improve the capability of a system through external perturbations. By means of the differences of antifragility between the original and mutated biological networks, we train a convolutional neural network (CNN) and test it to classify the properties of robustness and evolvability. We found that our CNN model successfully classified the properties. Thus, we conclude that our antifragility measure can be used as a
significant predictor of the robustness and evolvability of biological networks.

Source: arxiv.org (https://unam.us4.list-manage.com/track/click?u=0eb0ac9b4e8565f2967a8304b&id=14a50b195e&e=55e25a0e3e)


We are looking for a research scientist to help run the Observatory on Social Media (OSoMe, pronounced awe•some) at Indiana University Bloomington (IUB). The official title of the position is Senior Project Coordinator (SPC). The Senior Project Coordinator will join the OSoMe senior management team — director Filippo Menczer, co-directors for research Betsi Grabe and Alessandro Flammini, co-directors for education Elaine Monaghan and John Paolillo, Dean James Shahahan, and associate director for technology Val Pentchev. The mission of the Observatory, which recently received a $6 million investment from the John S. and James L. Knight Foundation and Indiana University, is to study the media and technology networks that drive the online diffusion of dis/mis/information. OSoMe offers access to data and tools for researchers worldwide to uncover the vulnerabilities of the media ecosystem and develops methods for increasing the resilience of citizens and democratic systems to manipulation.

Source: cnets.indiana.edu (https://unam.us4.list-manage.com/track/click?u=0eb0ac9b4e8565f2967a8304b&id=b68574d3dc&e=55e25a0e3e)



A First Course in Network Science

https://unam.us4.list-manage.com/track/click?u=0eb0ac9b4e8565f2967a8304b&id=2df78fdebb&e=55e25a0e3e

The book A First Course in Network Science (https://unam.us4.list-manage.com/track/click?u=0eb0ac9b4e8565f2967a8304b&id=789217f32b&e=55e25a0e3e) by CNetS faculty members Filippo Menczer and Santo Fortunato and CNetS PhD graduate Clayton A. Davis was recently published by Cambridge University Press (https://unam.us4.list-manage.com/track/click?u=0eb0ac9b4e8565f2967a8304b&id=f57805289a&e=55e25a0e3e) . This textbook introduces the basics of network science for a wide range of job sectors from management to marketing, from biology to engineering, and from neuroscience to the social sciences. Extensive tutorials, datasets, and homework problems provide plenty of hands-on practice. The book has been endorsed as “Rigorous” (Alessandro Vespignani), “comprehensive… indispensable” (Olaf Sporns), “with remarkable clarity and insight” (Brian Uzzi), “accessible” (Albert-László Barabási), “amazing… extraordinary” (Alex Arenas), and “sophisticated yet introductory… an excellent introduction that is also eminently prac
tical” (Stephen Borgatti). It was ranked by Amazon (https://unam.us4.list-manage.com/track/click?u=0eb0ac9b4e8565f2967a8304b&id=1fed456961&e=55e25a0e3e) #1 among new releases in mathematical physics.

Source: cnets.indiana.edu (https://unam.us4.list-manage.com/track/click?u=0eb0ac9b4e8565f2967a8304b&id=d4c0d9ba82&e=55e25a0e3e)


You can contribute to Complexity Digest selecting one of our topics (https://unam.us4.list-manage.com/track/click?u=0eb0ac9b4e8565f2967a8304b&id=88fbf2bfc4&e=55e25a0e3e ) and using the "Suggest" button.
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